Adaptive observer-based integral sliding-mode control for performance-bounded knee rehabilitation exoskeleton tracking

Wireless and programmable electrochemical soft actuators of ionogels for tracking and liquild control

Distributed tracking control for nonlinear strict-feedback systems with high-order filtering under malicious link attacks

This article investigates the problem of visual tracking of an unknown moving target by a network of robotic manipulators equipped with uncalibrated eye-in-hand cameras. The objective is to ensure that, for each robot, the target's projection is maintained at a specified position on the image plane, despite the uncalibrated camera parameters and uncertain, time-varying feature depths. The target's motion is assumed to be generated by a neutrally stable linear system, whose state and system matrix are not directly accessible to all robots. To address this problem, a distributed control scheme is developed in three steps. First, an adaptive distributed observer is introduced to estimate the motion of the moving target. Second, a novel image-space observer is designed for each robot to estimate the image-space position and to simultaneously provide the estimated image-space velocity, based on which the proposed distributed controller avoids using image-space velocity measurements. Third, by leveraging the linearly parameterized properties of the depth-independent image Jacobian matrix and the depth, adaptive laws are proposed to cope with uncertain parameters in cameras and robots. By using the Lyapunov stability theory, a rigorous analysis is provided to show the stability of the closed-loop system and asymptotic convergence of the image-space tracking errors. The effectiveness of the proposed scheme is illustrated through simulation with a group of three-DOF robotic manipulators.

Guaranteed-Performance tracking control for Stochastic-Disturbed robotic manipulators under limited communication

Adaptive deep Koopman-based MPC for trajectory tracking control of distributed drive electric vehicles considering dynamic motion requirements

Robust safety predictive tracking control for batch processes: deception attacks in unreliable network channels

Event triggered disturbance observer based nonlinear MPC for agricultural machinery trajectory tracking control

Tunable predefined-time trajectory tracking control for cable-driven space manipulators using extended state observer via fully-actuated system approaches

By means of a fully actuated system (FAS) approach, this article is concerned with an anti-disturbance tracking control problem toward a class of lumped disturbances containing the model uncertainties and external disturbances. A FAS predictive control with a generalized proportional-integral observer (GPIO) is presented to address this problem. Concretely, a FAS model of discrete-time nonlinear systems with the lumped disturbances is firstly given as a control-oriented one. Then, a GPIO is developed to achieve an accurate estimation for the lumped disturbances by adopting a less conservative disturbance assumption, which provides a better foundation to construct a disturbance preview. Furthermore, an incremental FAS (IFAS) prediction model with a disturbance preview is constructed by utilizing a new type of Diophantine Equation. Dependent on this IFAS prediction model, the multistep ahead predictions can be obtained to minimize an objective function to yield an optimal anti-disturbance controller, such that the desired tracking performance can be guaranteed. The depth analysis derives a sufficient condition for the bounded stability and tracking performance of the closed-loop FASs. The proposed GPIO-based FAS predictive control provides a solution to the spacecraft attitude control for verifying the feasibility.

Reinforcement learning-based optimal attitude tracking control for rigid spacecraft with external disturbances

Disturbance estimator-based tracking control for fractional-order Takagi-Sugeno fuzzy switched control systems

In complex real-world traffic environments, ramp merging tasks pose significant challenges for platoon control systems. Conventional control methods often lack the flexibility required for coordinated platoon merging scenarios and fail to adequately address inter-vehicle safety constraints. Consequently, developing control strategies that satisfy safety constraints while ensuring precise merging coordination becomes imperative. This paper proposes a platoon safety-oriented merging control scheme for autonomous vehicles (AVs) within an optimal control framework. First, we systematically investigate the definition and selection methodology of merging strategies in platoon merging scenarios. Leveraging this approach, a platoon merging performance function is formulated, with merging sequences determined through comparative evaluation of strategy-specific performance metrics. Subsequently, the safety constraints and terminal merging conditions are incorporated into the performance index design via optimal control theory, transforming the merging coordination and safety assurance tasks into an optimal control problem. Finally, simulation results validate the proposed tracking control scheme, demonstrating its reliability and effectiveness through comprehensive performance analysis.

Data-driven trajectory tracking control of UAV systems under a novel probability-selection event-triggered mechanism.

This article addresses the formation control of under-actuated multiple autonomous surface vehicles (MASVs) with input quantization under communication delay conditions, which is influenced by external marine disturbances and internal model uncertainties. A two-level distributed guidance and quantization control architecture based on the Nussbaum function is proposed. At the communication level, a time-delay distributed event-triggered extended state observer (ESO) is introduced to estimate the state of the single virtual leader, thereby further conserving communication resources. At the control level, the distributed formation guidance laws based on ESO are proposed in the kinematic subsystem, enabling effective tracking of the ideal trajectory while estimating the states of neighboring agents and unknown ocean disturbances. In the dynamics subsystem, a fuzzy logic system is used to estimate the uncertain terms within the model, and a linear model is introduced to handle the input quantization process. Additionally, the fuzzy adaptive quantization tracking control laws based on the Nussbaum function are proposed to achieve accurate tracking of the guidance signals and reduce actuator execution frequency, which makes the proposed scheme more applicable to practical marine engineering scenarios. The stability of the designed control structure is proven based on stability theory, and all signals within the closed-loop control system are uniformly ultimately bounded. Simulation experiments validate the rationality and effectiveness of the proposed method.

This paper investigates the asymptotic tracking control problem for multi-agent systems (MASs) under deception attacks, with unknown nonlinear dynamics and time-varying asymmetric state constraints. First, based on the backstepping control framework, a state-dependent function is introduced, and a new error coordinate transformation is defined to design a distributed controller, eliminating the feasibility condition requirement of virtual controllers in barrier Lyapunov function (BLF)-based methods. Second, an improved first-order nonlinear filter is designed based on the dynamic surface control (DSC) technique, which not only resolves the differential explosion problem caused by virtual controllers in the backstepping control method but also eliminates the adverse effects of unknown terms on the asymptotic stability. It can be proven that under the proposed control strategy, (1) asymptotic convergence of consensus errors is achieved, and (2) the states of MASs satisfy the preset constraints. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.

This paper investigates the problem of high-precision disturbance rejection tracking control for servo motion systems (SMS) with external disturbances and system uncertainties, where a control strategy combining the Fully Actuated System Approach (FASA), Generalised Extended State Observer (GESO), and Linear Quadratic Regulator (LQR) is proposed. First, a FASA model of the SMS is established with parameter matrices designed parametrically. Next, a GESO is developed to estimate system states and disturbances simultaneously. The LQR algorithm is then used to obtain disturbance compensation gains, enabling an FASA-based controller with estimation and compensation capabilities. Subsequently, Lyapunov stability theory is applied to verify closed-loop stability. Finally, simulation and experimental results show that the proposed method achieves higher precision than PD and uncompensated strategies under disturbances and uncertainties, can overcome limitations of traditional approaches, and improves control accuracy, stability, and reliability.

This paper proposes a nonlinear controller framework for the tracking and stabilization control of rotary inverted pendulum (RIP) systems. These systems present significant control challenges due to their underactuation, high sensitivity, and inherent instability. Most existing studies rely on linearized models of RIP systems. However, these approaches limit the controller's performance to a small area around the equilibrium point. On the other hand, applying advanced nonlinear control techniques to the RIP underactuated system remains challenging. To overcome these limitations, the second-order sliding mode controller (SOSMC) based on a PID sliding surface is developed and applied to the RIP system. The designed procedure incorporates the nonlinear dynamic model to stabilize the pendulum in a greater region and address the challenges of underactuation. The designed control scheme enhances the system performance in stabilizing the pendulum in the vertical upright position and maintaining its balance when the horizontal arm tracks a desired trajectory. The efficacy of the designed controller is compared with three other control approaches: a standard sliding mode control (SMC), a reinforced PID-SMC, and a linear quadratic regulator (LQR). The simulation results demonstrate that the proposed controller scheme exhibits high-speed tracking performance with reduced overshoot. The results show that the Mean Square Error (MSE) for the arm angle is decreased to 0.2229, compared to 0.22346 for reinforced PID-SMC, 0.2250 for SMC, and 0.2299 for LQR. Moreover, the MSE for the pendulum angle is reduced by approximately 69.36%, 28.6%, and 25.68% compared to the reinforced PID-SMC, SMC, and LQR algorithms, respectively. Furthermore, the designed controller achieves a 41.99%, 10.06%, and 18.33% reduction in the RMS value of the control input compared to the reinforced PID-SMC, SMC, and LQR controllers, respectively. The simulation results reveal that the designed controller achieves superior tracking accuracy, faster response, and reduced overshoot, while maintaining efficient energy consumption.

This article addresses the trajectory tracking control problem for four-wheel skid-steering small autonomous ground vehicles (FWSAGVs) subject to uncertain friction resistance, external disturbances, and parametric variations. Existing FWSAGV tracking methods usually focus on stability and robustness, but often lack explicit quantitative guarantees on transient and steady-state performance, or rely on relatively complex controller structures that are not well-suited for onboard implementation. To address this issue, a prescribed-performance fractional-order PI-like controller (PP-FOPIC) is proposed. By combining the prescribed performance control (PPC) with a low-complexity fractional-order PI-like structure, the proposed method explicitly constrains the tracking error evolution while avoiding complex online approximators and recursive backstepping designs. In addition, particle swarm optimization (PSO) is introduced to tune the key controller parameters automatically and reduce the subjectivity of manual tuning. Comparative simulations and real-world experiments demonstrate that the proposed method achieves better tracking accuracy, stronger robustness, and lower actuator overload than PP-IOPIC and proportional-integral-differential (PID). In particular, real-world hexagonal-trajectory experiments provide further evidence of its control effectiveness and practical applicability, with an average onboard execution time of 8.62 ms and a total actuator overload rate of 2.8740%.

Autonomous hydraulic excavators are widely used in construction, mining, and material-handling operations, offering improved efficiency and safety. Precise trajectory tracking is essential for such systems. However, inherent nonlinearities and significant time delays in the hydraulic actuators hinder accurate control for autonomous operations. To address these challenges, a nonlinear model predictive control (NMPC) algorithm is proposed. Specifically, a Hammerstein–Wiener structure is employed to model the nonlinear hydraulic system, with parameters identified from experimental data. Based on this model, an NMPC trajectory-tracking algorithm is developed, which accounts for actuator and control input constraints. To mitigate the intrinsic 0.5-second response delay of the hydraulic system, a predictive delay compensation strategy is introduced, whereby predicted joint states over the next 0.5 seconds serve as real-time control references. Simulation results demonstrate that the proposed controller substantially outperforms proportional–integral–derivative (PID) and fuzzy PID methods, maintaining the bucket-end error within 20 cm. Field experiments on an autonomous excavator implemented under the robot operating system (ROS) framework confirm that the maximum trajectory-tracking error remains within 50 cm, validating the effectiveness and robustness of the proposed NMPC approach under real-world operating conditions.